Satellite Image Time Series Analysis for Remote Sensing Data Cubes

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Documentation for package ‘sits’ version 0.14.0

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C P S T misc

sits-package sits

-- C --

cerrado_2classes Samples of classes Cerrado and Pasture
check_functions Check functions

-- P --

plot Generic interface for ploting time series
plot.classified_image Generic interface for ploting classified images
plot.keras_model Generic interface for plotting a Keras model
plot.patterns Generic interface for ploting patterns
plot.predicted Generic interface for ploting time series predictions
plot.probs_cube Generic interface for plotting probability cubes
plot.raster_cube Generic interface for plotting stack cubes
plot.sits Generic interface for ploting time series
plot.som_evaluate_cluster Plot information about confusion between clusters
plot.som_map Generic interface for plotting a SOM map
point_mt_6bands A time series sample with data from 2000 to 2016

-- S --

samples_modis_4bands Samples of nine classes for the state of Mato Grosso
samples_mt_6bands Samples of nine classes for the state of Mato Grosso
sits sits
sits_accuracy Area-weighted classification accuracy assessment
sits_accuracy.classified_image Area-weighted classification accuracy assessment
sits_accuracy.sits Area-weighted classification accuracy assessment
sits_apply Apply a function over a time series.
sits_bands Informs the names of the bands
sits_bands<- Replaces the names of the bands
sits_bbox Get the bounding box of the data
sits_classify Classify time series or data cube using machine learning models
sits_classify.raster_cube Classify time series or data cube using machine learning models
sits_classify.sits Classify time series or data cube using machine learning models
sits_cluster_clean Cluster cleaner
sits_cluster_dendro Clusters a set of time series using aglomerative hierarchical clustering
sits_cluster_frequency Cluster contigency table
sits_config sits configuration
sits_configuration sits configuration
sits_config_show sits configuration
sits_create_folds Create partitions of a data set
sits_cube_copy Creates the contents of a data cube
sits_data_to_csv Export a sits tibble data to the CSV format
sits_envelope Envelope filter
sits_filter General function for filtering
sits_formula_linear Define a linear formula for classification models
sits_formula_logref Define a loglinear formula for classification models
sits_from_zoo Import time series in the zoo format to a sits tibble
sits_get_data Obtain time series from different sources
sits_get_data.csv_raster_cube Obtain time series from different sources
sits_get_data.csv_satveg_cube Obtain time series from different sources
sits_get_data.csv_wtss_cube Obtain time series from different sources
sits_get_data.raster_cube Obtain time series from different sources
sits_get_data.satveg_cube Obtain time series from different sources
sits_get_data.shp_raster_cube Obtain time series from different sources
sits_get_data.shp_satveg_cube Obtain time series from different sources
sits_get_data.shp_wtss_cube Obtain time series from different sources
sits_get_data.wtss_cube Obtain time series from different sources
sits_impute_linear Linear imputation of NA values using C++ implementation
sits_interp Interpolation function of the time series of a sits_tibble
sits_keras_diagnostics Diagnostic information about a Keras deep learning model
sits_kfold_validate Cross-validate temporal patterns
sits_labels Returns the information about labels of a data set (tibble or cube)
sits_labels<- Change labels of a sits tibble
sits_labels_summary Returns the information about labels of a tibble data set
sits_label_classification Post-process a classified data raster probs to obtain a labelled image
sits_label_majority Post-process a classified data raster with a majority filter
sits_lda Train a sits classification model using linear discriminant analysis
sits_linear_interp Interpolation function of the time series in a sits tibble
sits_merge Merge two data sets (time series or cubes)
sits_metadata_to_csv Export a sits tibble metadata to the CSV format
sits_missing_values Remove missing values
sits_mlp Train a deep learning model using multi-layer perceptron
sits_mlr Train a sits classification model using multinomial log-linear
sits_ndwi Builds normalized difference water index
sits_patterns Create temporal patterns using a generalised additive model (gam)
sits_qda Train a classification model using quadratic discriminant analysis
sits_regularize Creates a regularized data cube from an irregular one
sits_ResNet Train a model using the ResNet model
sits_rfor Train a SITS classifiction model using random forest algorithm
sits_sample Sample a percentage of a time series
sits_savi Builds soil-adjusted vegetation index
sits_select Filter bands on a data set (tibble or cube)
sits_sgolay Smooth the time series using Savitsky-Golay filter
sits_smooth Post-process a classified data raster probs using smoothing
sits_smooth.bayes Post-process a classified data raster probs using smoothing
sits_smooth.bilateral Post-process a classified data raster probs using smoothing
sits_smooth.gaussian Post-process a classified data raster probs using smoothing
sits_som_clean_samples Clean samples
sits_som_cluster Clustering a set of satellite image time series using SOM
sits_som_evaluate_cluster Evaluate cluster
sits_som_map Generate a Kohonen map for sample quality control
sits_svm Train a sits classification model using a support vector machine
sits_TempCNN Train a model using the Temporal Convolutional Neural Network
sits_timeline Obtains the timeline
sits_time_series Retrieve time series for a row of a sits tibble
sits_to_xlsx Saves the results of accuracy assessments as Excel files
sits_to_zoo Export data to be used to the zoo format
sits_train Train sits classification models
sits_twdtw_classify Find matches between patterns and time series using TWDTW
sits_values Return the values of a given sits tibble as a list of matrices.
sits_view Generic interface for visualization of data cube
sits_view.classified_image Generic interface for visualization of data cube
sits_view.raster_cube Generic interface for visualization of data cube
sits_whittaker Filter the time series using Whittaker smoother
sits_xgboost Train a model with an extreme gradient boosting machine

-- T --

timeline_2000_2017 The timeline for the sequence of images for MOD13Q1 collection 6
timeline_2013_2014 The timeline for the sequence of images one year (2013 to 2014)
ts_zoo A time series in the ZOO format

-- misc --

%>% Pipe
.check_apply Check functions
.check_bands Check cube collection
.check_chr Check functions
.check_chr_contains Check functions
.check_chr_type Check functions
.check_chr_within Check functions
.check_collection Check cube collection
.check_env_var Check functions
.check_error Check functions
.check_file Check functions
.check_identify_caller Check functions
.check_length Check functions
.check_lgl Check functions
.check_lgl_type Check functions
.check_lst Check functions
.check_lst_type Check functions
.check_na Check functions
.check_names Check functions
.check_null Check functions
.check_num Check functions
.check_num_type Check functions
.check_set_caller Check functions
.check_that Check functions
.check_warn Check functions
.stac_bands_select Select stac items by sits bands.
.stac_items_query Creates a query to send to STAC api
:= Set by reference in data.table
_PACKAGE sits
`sits_bands<-` Replaces the names of the bands
`sits_labels<-` Change labels of a sits tibble